1.3Standard
Move a model from development to production.
Track data, code, model, and evaluation and be able to recover.
Choose two operational practices.
× Incorrect
Version data, code, model, settings, and results, and monitor quality, drift, and cost with retraining and rollback criteria.
Detailed explanation
CorrectMonitor quality, drift, and cost after deployment and define retraining and rollback criteria
Production degradation can be handled.
IncorrectStop evaluation and monitoring after deployment
Data and service conditions change.
IncorrectEdit the model directly in production
Changes cannot be reproduced or reviewed.
CorrectVersion data, code, model, settings, and evaluation results
Changes can be reproduced and investigated.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 1.3の機械学習ライフサイクル、MLOps、監視を確認する。
Official references
What to confirm
モデル開発と本番運用を連続したライフサイクルとして説明できる。
Key points
- Versioning
- Monitoring
- Rollback
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
MLOps
Practices for managing machine-learning development, evaluation, deployment, monitoring, and retraining.
Model registry
A catalog of model versions, evaluations, approvals, and deployment status.
Question IDAWS-AIF-254
EnvironmentAWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
Last technical review2026-08-20
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